Artificial Intelligence
Diffusion Subgoal Planning for Long-Horizon Offline Goal-Conditioned Reinforcement Learning
Hengrui Zhang, Yuhu Cheng, C. L. Philip Chen et al. (+1 more)
Offline goal-conditioned reinforcement learning (GCRL) learns goal-directed policies from reward-free data, but in long-horizon tasks, goal-conditioned value functions often provide unstable guidance due to sparse rewards and discounting. Hierarchical methods partially mitigate this issue via subgoal decomposition; however, high-level decision-making still relies on noise-sensitive value estimates, leading to unstable behavior in complex environments. We address this limitation by proposing
Diffusion Subgoal Planning (DSP), a
diffusion-based framework for high-level subgoal generation. DSP casts high-level planning as guided generative inference over goal-conditioned subgoals and learns both conditional and unconditional flows, enabling classifier-free guidance to introduce a goal-directed bias at inference time. By removing explicit value-based guidance from high-level planning, DSP generates reachable and goal-directed subgoals through a generative model while retaining hierarchical execution. Experiments on offline GCRL
benchmarks demonstrate that DSP outperforms prior methods on a range of
navigation and manipulation tasks, with particularly strong performance in maze environments that require multi-step subgoal planning.
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All authors Hengrui Zhang, Yuhu Cheng, C. L. Philip Chen, Xuesong Wang
Systems and Control
Robust Variable-Horizon MPC for Landing a Multirotor UAV on a Moving Platform
Sander Doodeman, Niels Berkers, Paula Chanfreut et al. (+2 more)
Landing a multirotor Unmanned Aerial Vehicle (
UAV) on a moving platform is challenging because a
UAV is underactuated and the desired landing state is generally a non-
equilibrium state. Shrinking- and variable-horizon approaches are promising for reaching such non-
equilibrium targets, but often lack robustness to disturbances. Robust Variable-Horizon Model Predictive Control (VH-MPC) addresses this limitation but is computationally complex for a high-dimensional system such as a multirotor
UAV. This paper presents a computationally efficient robust Variable-Horizon MPC method for reaching non-
equilibrium targets under bounded disturbances. The online horizon search is restricted to a neighborhood of the previously selected horizon, while maintaining recursive feasibility under bounded disturbances. A fixed robust positively invariant tube provides horizon-independent constraint tightening. By exploiting the differential flatness property of a
UAV, this approach is applied to a decoupled linearized system, enabling real-time implementation on an onboard Raspberry Pi 5 at 20 Hz. Simulations show that restricting the horizon search substantially reduces computation with limited impact on the objective, while real-time Gazebo simulations demonstrate the robustness of the method. Physical experiments demonstrate successful landing of a multirotor
UAV on a moving Stewart platform.
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All authors Sander Doodeman, Niels Berkers, Paula Chanfreut, Elena Torta, Duarte Antunes
Artificial Intelligence
Nudgeability: Reasoning Models Follow Confidence Signals Without Tracking Their Own Competence
Rohit Saxena and Utkarsh Upadhyay
Reasoning language models that can call tools must decide during inference whether to answer unaided or delegate. Any self-reflection mechanism for this must answer three questions: where the reflective signal comes from (verbal reports, output distributions, hidden states, a separate predictor), how it is presented to the model (numerical prediction, confidence
token,
prompt injection), and whether it changes the model's subsequent action. We isolate the third question. At a fixed point in otherwise identical
reasoning trajectories, we insert a single first-person sentence expressing either confidence or doubt; the model then continues
reasoning and chooses whether to answer directly or call a tool. Comparing these counterfactual continuations measures the causal effect of the reflective signal on delegation. We call this behavioral response Nudgeability and measure it along two dimensions: sensitivity, how strongly confidence and doubt change delegation rates, and targeting, whether delegation increases for problems the model cannot solve unaided and decreases for those it can. Across nine small-to-medium open-weight
reasoning models from three families (Qwen, Gemma, and GLM) and two tasks, models are consistently sensitive: doubt increases delegation and confidence decreases it, with a median confidence-to-doubt swing of 20.6 percentage points, and 53 to 70 points for the larger provider-served models. This responsiveness is poorly targeted: a median 42% of induced flips are well-targeted, only a +2 percentage-point lift over a random-selection baseline. Confidence
language is thus a strong control surface for delegation, but current models use it only weakly in accordance with their actual competence. Nudgeability offers a simple, post-training-free way to evaluate both sensitivity and targeting as endogenous self-reflection mechanisms mature.
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Artificial Intelligence
PersonaManifold: Revealing and Exploiting Curved Geometry in LLM Persona Representations
Rui Xu, Yinghui Xu and Libo Wu
Controlling persona in large
language models (LLMs) at inference time is important for role-playing, personalized dialogue, and social simulation. Recent methods extract persona vectors from the model's activation space and apply Euclidean operations—addition, scaling, and linear interpolation—under the linear representation hypothesis. However, these methods themselves report systematic failures: non-orthogonal trait dimensions, asymmetric ceiling and resistance effects, and significant deviations in multi-trait composition, suggesting that the linear isotropic assumption does not hold. We propose PersonaManifold, a framework that models persona representations as points on a curved, low-dimensional Riemannian submanifold in activation space. We estimate the
manifold's intrinsic geometry—local metric tensors, geodesic distances, and Ollivier-
Ricci curvature—and introduce geodesic steering, which interpolates between personas along
manifold geodesics rather than Euclidean straight lines. We also propose the Behavioral Similarity Triplet (BST)
benchmark, which automatically generates situational questions grounded in six established psychological constructs and defines persona similarity through behavioral responses rather than self-report questionnaires. Experiments on three open-source LLMs show that persona activations form a
manifold with heterogeneous
curvature, geodesic distance predicts behavioral similarity more accurately than Euclidean alternatives with independent contributions from anisotropy and
curvature, and geodesic steering produces more coherent intermediate personas on both our BST
benchmark and external evaluations, with the advantage concentrated in high-deviation regions where the
manifold deviates most from flatness.
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Information Theory
Contraction of Rényi Divergences for Discrete Channels
Adrien Vandenbroucque, Amedeo Roberto Esposito and Michael Gastpar
We investigate Strong Data-Processing Inequality (SDPI) constants for Rényi Divergences on finite spaces. We study their dependence on the Rényi order $\alpha$, proving that they are non-decreasing and that their scaling by $(\alpha-1)$ is convex for $\alpha\geq1$. We also identify several support restrictions on the probability measures involved in determining these constants. In particular, in the distribution-independent setting, measures supported on a common set of at most two points suffice to evaluate the Rényi-SDPI constant. For $\alpha\in[0,1]$, we further prove equality with the $\chi^2$-SDPI constant, while at order infinity we obtain a closed-form expression. In order to link contraction over product spaces to contraction along individual coordinates, we provide tensorisation bounds for arbitrary product channels analogous to those known for $\varphi$-Divergences. At finite orders, the Rényi-SDPI constants are bounded above and below through comparisons with the $\chi^2$-Divergence and Hellinger Divergences, with sharpness established in multiple cases. At order infinity, we instead relate these constants to the contraction of Total Variation Distance. Finally, our findings are applied to local differential
privacy (LDP) and the analysis of Markov chains. This yields sharp contraction guarantees for pure-LDP mechanisms, and connects Rényi-LDP to Rényi-SDPI constants. For Markov chains, we derive finite-time convergence bounds and exhibit a family of chains for which Rényi-SDPIs improve on classical $\chi^2$-based bounds by arbitrarily large factors.
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Computer Vision
Recent Advances in Agentic Agri-Robotic Phenotyping: A Perspective Review from Fragmented Multimodal Sensing to Unified PhenoAgent Intelligence
Muhammad Owais, Ehtesham Iqbal, Samee Ullah Khan et al. (+3 more)
This review examines the
evolution of plant phenotyping from conventional manual trait measurement to high-throughput, robotic, and artificial intelligence-driven crop monitoring. Despite significant advances in imaging, autonomous platforms,
multimodal sensing, and deep learning, current phenotyping systems remain fragmented across sensing modalities, crop traits, growth stages, environments, and management objectives. We therefore frame phenotyping as an integrated seed-soil-plant-environment-management (SSPEM) intelligence problem, where crop performance reflects interactions among seed quality, root-zone conditions, plant development, environmental exposure, and management actions. The review synthesizes conventional, high-throughput, robotic, and AI-driven phenotyping approaches, highlighting their capabilities and persistent limitations in temporal integration,
multimodal reasoning, biological interpretation, and actionable decision support. Building on this analysis, we introduce a conceptual PhenoAgent framework that extends phenotyping beyond the estimation of isolated traits to evidence-based crop-state interpretation, uncertainty-aware
reasoning, and management-oriented support. The PhenoAgent concept primarily brings together scattered advances in phenotyping to deliver insights ranging from detailed to high-level, such as what is happening in the crop, why it might be occurring, what evidence is missing, and what actions or additional measurements should be considered. We also discuss challenges in
dataset scarcity, annotation, benchmarking, model generalization, and explainability. By linking
multimodal phenotyping with agentic AI and closed-loop decision support, this review outlines a path to interpretable, scalable, and deployment-oriented crop intelligence.
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All authors Muhammad Owais, Ehtesham Iqbal, Samee Ullah Khan, Muhammad Umraiz, Yusra Abdulrahman, Irfan Hussain
Machine Learning
Retracing Hodgkin and Huxley: State Recovery Does Not Certify Mechanism
Peiyu Zang, Jiayi Hao and Yongqiang Cai
Predicting observed dynamics does not establish recovery of the underlying physical mechanism. Can machine learning retrace the hidden-state
reasoning behind the Hodgkin-Huxley (HH) model? We train structured latent models on simulated current and voltage, withholding gate identities and trajectories from training and model selection. We then test response prediction, state recovery, protocol transfer, and agreement with HH dynamics. Prediction error and its cross-seed spread both drop sharply at three latent dimensions under the tested protocols, while gate recovery under new protocols improves through five to six coordinates. State recovery depends on which observations the chart uses. Observed voltage improves current-clamp decoding relative to freely predicted voltage. Under voltage clamp, adding latent state to command voltage raises m-state $R^2$ from 0.976 to above 0.99, yet the transported field disagrees with HH on identical smooth samples. Known invertible HH coordinates achieve high fast-m field agreement under the same audit procedure. An exact HH identity decomposes the discrepancy into time-scale-weighted state error and a residual in the transported field; these terms can cancel or reinforce. These findings concern the tested models and charts. They support evaluating state and dynamics recovery separately, including chart inputs and transported-field agreement across interventions.
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Artificial Intelligence
FlowState: Execution State as Memory for Long-Horizon LLM Agents
Minghao Li, Bangyan Li, Zifan Wang et al. (+5 more)
Long-horizon tasks require LLM
agents to continually draw on information from earlier interactions. However, retaining the full history increases context costs, while compressing it risks losing details needed later, and the relevance of historical information often becomes apparent as the task progresses. To address these challenges, we propose FlowState, which treats execution state as
memory that can be retained and revisited across requests, unifying current decision-making with the reuse of historical information. FlowState preserves semantically typed state nodes, their relations, and references to raw tool observations, separating persistent retention from on-demand access. Within a single execution loop, Incremental State Update (ISU) maintains the current state based on new inputs and feedback, while Progressive State Access (PSA) progressively reveals historical states and supporting evidence as needed during
reasoning. Together, these mechanisms enable
agents to reassess prior decisions in light of new information and guide subsequent actions. Compared with a full-context baseline using the same DeepSeek-V4-Flash model, FlowState improves the average success rate on MemoryArena and the average pass rate on $\tau^3$-Bench by 4.55 and 13.95 percentage points, respectively, while reducing total
token consumption by 43.2% and 40.6%. These results demonstrate the performance and efficiency advantages of FlowState on long-horizon tasks.
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All authors Minghao Li, Bangyan Li, Zifan Wang, Yulong Li, Hu Xu, Gan Zhang, Jingtong Wu, Wenqiang Xu
Computer Vision
GLF-Q: Global-Local Feature-based Quantization for Vision Transformers
Peilin Sun, Guang Liang, Jin Tong et al. (+1 more)
Post-training quantization (PTQ) efficiently compresses
Vision Transformers (ViTs) without retraining, yet suffers severe accuracy degradation at low bit-widths. Existing optimization-based PTQ methods guide block reconstruction via either soft logits or second-order Hessian proxies. Logit supervision is prone to overfitting on limited calibration data, while Hessian approximations incur structural truncation errors. To address these limitations, we propose GLF-Q, a novel PTQ framework guided by Global-Local Feature
alignment. GLF-Q propagates quantized block outputs through downstream full-precision layers to align penultimate-layer representations under local output regularization, providing downstream feature supervision without explicitly approximating the Hessian or using a Taylor expansion. Furthermore, offline Hadamard transformations are introduced with zero runtime overhead to disperse activation outliers across channels, effectively contracting dynamic ranges and reducing quantization errors. Meanwhile, optimizing this loss via a Straight-Through
Estimator (STE) achieves rapid convergence, bypassing continuous relaxation rounding formulations such as AdaRound. Extensive experiments across representative ViT architectures demonstrate that GLF-Q with standard uniform quantizers substantially outperforms state-of-the-art methods under 3-bit quantization on image classification. In addition, GLF-Q exhibits strong out-of-domain calibration robustness and achieves speedups under 8-bit GPU deployment.
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All authors Peilin Sun, Guang Liang, Jin Tong, Jianxin Wu
Computer Vision
Rethinking Latent Visual Reasoning: Grounding Latent Reasoning in Visual Evidence
Xi Xiao, Tianchen Zhao, Youngeun Kim et al. (+10 more)
Latent visual
reasoning (LVR) enables
multimodal large
language models (MLLMs) to perform intermediate computation in continuous latent
tokens rather than expressing every
reasoning step in words. However, unlike textual CoT, latent
reasoning is not directly observable, making it difficult to supervise what latent
tokens learn. In this work, we first conduct a thorough analysis of latent-
token behavior and identify a latent evidence-credit gap: latent
tokens respond only weakly to image perturbations that alter the correct answer. We hypothesize that this issue stems from the lack of explicit supervision during GRPO training. These findings suggest that a final-answer reward provides too little guidance on what visual evidence to preserve or how credit should be assigned across latent
tokens. To bridge this gap, we propose ReaLVR, which brings visual-evidence supervision to the model's own free-running latent trajectories. ReaLVR contrasts correct and model-generated wrong answers to determine where stronger supervision is needed, and relevant and mismatched visual evidence to specify what to preserve. Across three model families, ReaLVR consistently outperforms evaluated LVR baselines, achieving the highest five-task average of 63.7% on Qwen2.5-VL-7B. Crucially, we are the first to scale visual
reasoning in latent space, showing that our framework continues to deliver robust improvements at frontier model scales up to 235B. Further analyses show more question-sensitive latent-
token positions, stronger
alignment with relevant visual regions, and greater fixed-context dependence on the most attended latent
tokens.
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All authors Xi Xiao, Tianchen Zhao, Youngeun Kim, Zhuowei Li, Linghan Xu, Jiaye Wu, Zheng Zhang, Xiang Xu, Xuanbai Chen, Farhan Tejani, Jakub Zablocki, Julia Xu, Yifan Xing
Machine Learning
Single-Layer MeMo as a Randomized Hamming-Kernel Classifier
Alessandro Straziota
MeMo (Zanzotto et al., 2025) is a recent
language-model architecture that stores associations between
token contexts and next
tokens in a correlation
matrix memory. In this work, we study its single-layer form and show that its ideal
retrieval rule is a multiclass classifier based on the positional Hamming kernel. The MeMo architecture represents both the sequence features and the output labels with Gaussian random codes. Its score is therefore a doubly randomized sketch of the ideal classifier. Under independent input and output codebooks, we bound the errors introduced by context sketching and output decoding, characterize their dependence on model and data parameters, and give a margin-based guarantee for recovering the ideal prediction. Controlled simulations support the trends predicted by the analysis. On a restricted WikiText-2 next-
token task, we compare single-layer MeMo with classical baselines and show that it can offer a useful trade-off among predictive accuracy,
memory, and throughput, particularly on a GPU, where its
matrix operations can be parallelized.
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Machine Learning
Brain-Conditioned Action Policies for Neural Motor Decoding
Luyao Jin, Running Zhao, Huan Zhao et al. (+2 more)
Motor
brain-computer interfaces (BCIs) aim to decode motor intention, enabling people with paralysis to control external devices. Neural motor decoding typically learns task-specific mappings from neural activity to kinematics, yet remains constrained by scarce paired neural-action data. We propose BrainVLA, a framework that enables neural motor decoding by drawing on a pretrained
vision-
language-action (VLA) model through
language-mediated
alignment. BrainVLA mitigates reliance on scarce paired neural-action data by leveraging VLA policies. We first construct VLA-compatible
datasets including paired neural activity, action signals,
language instructions, and rendered visual observations. Then, we adapt the OpenVLA-OFT policy to the target action spaces through LoRA fine-tuning. To establish an effective interface through which neural activity can convey motor intention to adapted VLA policies and guide action generation, we train a neural encoder via neural-
language alignment, using
language representations as semantic targets to capture latent motor intent from neural activity. The resulting neural representations serve as an endogenous intention signal to guide VLA policies to generate executable actions, while visual observations provide complementary information about the evolving task state. BrainVLA is evaluated on two neural motor
datasets with different action dimensionalities using causal rollout decoding. It outperforms the evaluated baselines in cross-session decoding $R^2$ and task success rate, while demonstrating high training data efficiency. These results establish a route for neural motor decoding to draw on large-scale robotic priors through
brain-conditioned VLA policies.
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All authors Luyao Jin, Running Zhao, Huan Zhao, Vincent C. K. Cheung, Wei-Hsin Liao
Formal Languages and Automata
Observer–Fragmentation–Exposure Tradeoffs: From Rectangular CFG Exposure to Ordered MCFG Scheduling
Takayuki Kuriyama
We study finite resources governing exact positive reconstruction in fixed-observation CFG and MCFG learning. For an explicit rigid CFG family we compute safe observation size, internal residual fragmentation, and characteristic-data cost exactly. The result is a two-point Pareto frontier: after compulsory
rigidity witnesses are fixed, exact reconstruction reduces to connectivity inside observer fibers, and the variable part of every minimum characteristic sample is a spanning forest of complete bipartite fiber graphs. For bounded-fan-out MCFG reconstruction, pure transition fragmentation multiplies across children. An explicit fan-out-two family X_{k,r} therefore has exact characteristic-data costs 2r[1+r(k-1)] and 2rk^r under two comparable observers. An integral
lattice invariant yields an affine-span lower bound and a unimodularity test. In the binary-index subfamily, unimodularity suffices for minimum-cardinality samples through ranks two and three but not rank four. Two distinct obstructions then appear: an order-independent laminar support conflict, and an order-sensitive occurrence-scheduling conflict. For disjoint child requirements, the exact scheduling threshold is the largest monochromatic run count in the doubled reduced slot-colour word; in the two-colour case this is an alternation threshold. Horn nonlocking and Cartesian locking certificates make these constraints explicit. Hierarchical reuse can trade parent-root exposure for local fan-out: for the natural critical-module library of the mixed rank-four shapes, the exact width–anchor frontiers are {(2,1)}, {(2,2),(3,1)}, and {(4,1)} for separated, nested, and crossing orders. Thus observation, fragmentation, exposure, arithmetic span, laminar compatibility, ordered scheduling, and hierarchical reuse are genuinely distinct finite resources.
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Computer Vision
ACPruner: Visual Token Pruning as Biased Attention Coverage Maximization in LVLMs
Xu Li, Yuxuan Liang, Yi Zheng et al. (+6 more)
Large
Vision-
Language Models (LVLMs) face significant computational inefficiencies caused by the large number of visual
tokens. Existing visual
token pruning methods mainly focus on either retaining individually important
tokens or selecting mutually diverse ones. In this work, we revisit visual
token pruning from a coverage perspective and formulate it as a biased
attention coverage maximization problem. The key idea is to select a compact
token subset whose encoder-side outgoing
attention can jointly cover the image while assigning higher coverage priority to more informative regions. From this perspective, we propose ACPruner, a training-free visual
token pruning framework for efficient LVLM inference. ACPruner first estimates
token importance by combining intra-modal saliency and inter-modal relevance, then derives
token-wise coverage from
attention patterns within the
vision encoder, and finally performs greedy selection to maximize the proposed coverage objective. Extensive experiments across multiple LVLM backbones, including LLaVA-1.5-7B/13B, LLaVA-NeXT-7B/13B, Qwen2.5-VL-7B, and LLaVA-OneVision-7B, show that ACPruner consistently achieves strong performance retention while delivering substantial end-to-end inference speedups.
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All authors Xu Li, Yuxuan Liang, Yi Zheng, Zhe Liu, Xiaolei Chen, Haotian Chen, Rui Zhu, Fan Shi, Xiangyang Xue
Artificial Intelligence
SkillRubric: Co-Evolving Actor Guidance and Evaluator Rubrics for Multimodal Agents
Bingqing Jiang, Guoxi Zhang, Jasper Wang et al. (+7 more)
Recent work incorporates reusable skills distilled from past interactions into
multimodal agent training, providing procedural guidance for long-horizon planning and tool use. However, policy optimization in these methods remains driven primarily by sparse outcome rewards, providing little supervision for intermediate decisions. Rubric-based rewards address this limitation through explicit intermediate criteria, but reliable rubrics are difficult to construct at scale and often disconnected from the procedure followed by the actor. We observe that a well-structured skill naturally specifies both how to act and what successful execution should achieve. Based on this insight, we introduce SkillRubric, which represents each skill through aligned actor-facing guidance and an evaluator-facing rubric. A
multimodal verifier evaluates skill-defined goals using screenshots and tool outputs, assigning completion and progress rewards to the responsible turns. We further introduce an alternating co-
evolution scheme that validates guidance revisions through paired rollouts under a frozen policy and rubric revisions offline under fixed guidance. Experiments across diverse
multimodal agent benchmarks demonstrate consistent performance gains, while controlled paired rollouts further show that evolved skills provide more effective guidance for planning and tool use than their preceding versions.
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All authors Bingqing Jiang, Guoxi Zhang, Jasper Wang, Auric Wang, Bingning Wang, Tianyi Lin, Zichao Yu, Yujin Han, Ziye Ma, Difan Zou
Computation and Language
RoPE is Dead, Long Live RoPE: Towards Scalable Data-aware Positional Encodings
Jarod Lévy, Mathurin Videau, Jad Yehya et al. (+3 more)
Transformers process
tokens without any inherent notion of order, making positional encoding a fundamental requirement rather than an architectural refinement. Rotary Position
Embedding (RoPE) has become the default positional encoding in modern
language models, yet it is heavily biased toward nearby
tokens. Existing alternatives have been evaluated under different settings, leaving the literature fragmented and without a clear replacement. We bring structure to this landscape by examining a specific weakness of RoPE: its slow frequency bands, whose wavelengths exceed the training context and expose models to unseen angles during extrapolation. We therefore introduce Data aware RoPE (DaRoPE), which preserves standard RoPE on the fast bands but replaces absolute position on the slow bands with bounded coordinates learned from contextual representations. Therefore, the slow-band geometry depends on the data rather than only on positional distance. We compare representative encodings under matched conditions across synthetic tasks, symbolic music, genomics, neural signals, and
language models spanning 124M to 50B parameters. Across these experiments, DaRoPE leads on non-text
benchmarks, mitigates recency bias, while remaining best or on par in
language modeling and length extrapolation. Moreover, the learned coordinates also make the mechanism interpretable, revealing how
attention layers leverage contextual information beyond
token distance. Together, these results support DaRoPE as the best overall default among the evaluated methods, when there is no domain-specific reasons to prefer another.
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All authors Jarod Lévy, Mathurin Videau, Jad Yehya, Jean-Rémi King, Stéphane d'Ascoli, Thomas Moreau
Machine Learning
PulseInfer: I/O-Centric Sparse KV Cache Offloading for Efficient Long-Context LLM Decoding
Qiuyang Zhang, Kai Zhou, Kai Lu et al. (+6 more)
Long-context LLM serving is increasingly bottlenecked by decode, where large KV caches limit batch size and underutilize GPUs. Sparse KV cache offloading expands effective capacity by storing most historical KV blocks in CPU DRAM and recalling only selected blocks on demand. However, we find that existing offloading systems shift the bottleneck to CPU-GPU recall I/O: recall volume varies widely across layers, decode steps and requests, while headwise sparse selection fragments recalls into many small PCIe transfers. This paper presents PulseInfer, an I/O-centric sparse KV cache offloading system. PulseInfer hides variable recall latency with interruptible layer-wise scheduling, adapts offloading decisions with IO-Adaptive Offloading Admission, and coalesces fragmented transfers using SoloHead sparse selection and a gather-scatter I/O engine. Implemented on SGLang, PulseInfer improves decode throughput by up to 4.7x over SGLang and 2.6x over the best existing offloading baseline, while reducing TPOT by up to 76% and preserving near-lossless accuracy.
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All authors Qiuyang Zhang, Kai Zhou, Kai Lu, Haocheng Lu, Jian Zhou, Yuanpeng Su, Kun Bao, Jiguang Wan, Fei Wu
Robotics
Where Memory Belongs: Ledger, an Object Ledger for Memory-Augmented VLAs
Tanguy Dieudonné, Jack B. Jedlicki and Heng Yang
Memory is essential for long-horizon, partially observed robotic manipulation: a
robot must remember which object was placed in a drawer, whose cup it moved, or how many action cycles have elapsed. Recent
vision-
language-action (VLA) models embed
memory directly inside the policy, but
benchmarks show no single in-policy mechanism covers all spatio-temporal dimensions, trailing oracle methods by a wide margin. We argue that
memory type dictates where
memory should reside: short-term perceptual
memory (repetition, timing, retracing) belongs inside the policy, while long-term object
memory (persistent spatial state, containment, event history) belongs outside as an explicit, readable record. We present Ledger, a harness that realizes this split over a single fine-tuned $\pi_{0.5}$ policy by pairing an in-policy frame-sampling
memory with an external spatio-temporal object
memory, the ledger, built from a SAM3 tracker and a VLM captioner of the demonstration and read by an LLM planner that decides at step boundaries. On RoboMME, Ledger reaches the highest four-suite average among the evaluated methods, 64.3% (vs. 45.9% for the strongest prior method under identical evaluation), leading object reference (60.7% vs. 40.3%) and object permanence (86.7% vs. 56.2%) using a single set of weights. Choosing the
memory source at runtime, from the instruction and the record, removes the need for a task-level router.
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Machine Learning
Verifying Neural Networks with Reinforcement Learning
Hai Duong, Thanh Le and ThanhVu Nguyen
Formal verification can play a key role in ensuring the reliability of Deep Neural Networks (DNNs) deployed in safety-critical systems. Modern DNN verifiers employ a branch-and-bound framework, which alternates between branching (splitting into smaller subproblems) and bounding (
pruning subproblems) to efficiently explore the verification space. However, existing branching heuristics make greedy decisions based on static scoring functions. They do not anticipate long-term efficiency or leverage the growing availability of verification data to improve performance. This work introduces RSB, a reinforcement learning framework that learns to refine baseline branching heuristics. It trains an actor-critic architecture to maximize cumulative future rewards rather than immediate scores. The actor generates
attention weights from observations of raw
neuron features and learned graph
embeddings, which rescale baseline heuristic scores to guide
neuron branching. Evaluation on 600 challenging instances demonstrates that RSB consistently outperforms state-of-the-art branching heuristics, solving 11% more instances while reducing branch exploration by 50%.
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Information Retrieval
EvoSkillRec: Skill-Genome Evolution for Recommender Architecture Discovery
Xiaopeng Li, Kuo Cai, Bo Chen et al. (+12 more)
Modern recommender systems advance not only by scaling data and parameters, but also by encoding task-specific inductive biases through architecture, including sparse feature interactions for click-through rate (CTR) prediction, temporal
attention for sequential recommendation, and expert routing for multi-task learning. However, these biases are typically human expert designed or searched within predefined operator spaces. Although Recent LLM-driven code
evolution expands this space, unconstrained edits often produce invalid or ineffective architectures, underuse established architecture design knowledge, and fail to preserve successful innovations for reuse. We introduce EvoSkillRec, a promotion-and-reuse framework for cumulative recommender architecture
evolution. It first decomposes recommenders into atomic executable skills and represents architectures as typed skill genomes, with each skill equipped with input–output types, semantic annotations, and implementation code. We then evolve models with different tasks through two coupled spaces: a constrained skill–space that mutates, recombines, specializes, and reuses validated skills, and an open-ended code–space in which LLM planners and synthesizers invent new skill modules using prior
evolution traces and accumulated experience. An autoresearch controller evaluates candidates, diagnoses failures, retrieves relevant skills, promotes validated innovations into the skill library, and adaptively allocates the proposal budget between the two spaces. Extensive experiments on CTR prediction, multi-task learning, and multi-domain learning, including resource-constrained co-optimization of predictive quality and model FLOPs utilization in generative ranking models, consistently demonstrate the effectiveness of our proposed EvoSkillRec.
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All authors Xiaopeng Li, Kuo Cai, Bo Chen, Wenlin Zhang, Mengyang Ma, Yingyi Zhang, Zichuan Fu, Yu Yang, Qidong Liu, Yiyu Wang, Ruiming Tang, Wenwu Ou, Jiang Wu, Zhanbo Xu, Xiangyu Zhao
Robotics
Yihan Zhou, Rui Yan, Mingcong Li et al. (+4 more)
Vision-
Language-Action (VLA) fine-tuning pairs images with actions at every step, yet typically provides only a task-level
language instruction, leaving moment-to-moment visual relevance implicit. We introduce eye-tracker-supervised gaze prompting, which uses gaze recorded during VR teleoperation to provide frame-level visual guidance for VLA fine-tuning. During training, recorded gaze locations are rendered as crosshairs on the
robot's head-camera images. At deployment, a lightweight predictor estimates gaze locations from recent images and the instruction, supplying the same type of visual
prompt without an eye tracker or changes to the policy architecture. Instantiated with $\pi_0$, gaze prompting increases mean success from $26.3\%$ to $56.0\%$ across six real-world bimanual manipulation tasks, with gains also observed when a single policy is trained on all six tasks. We release GazeMani, a
dataset of $1{,}200$ teleoperated trajectories with synchronized gaze.
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All authors Yihan Zhou, Rui Yan, Mingcong Li, Zheyuan Huang, Xu Yang, Xueyang Guo, Yilin Mo
Machine Learning
On Parameter Symmetries and Conservation Laws in Gradient Flow
Khang Nguyen and Guido Montúfar
Parameter space
symmetries and conservation laws play an important role in understanding the loss landscapes and implicit biases of neural networks. Inspired by Noether's
theorem in physics, prior works have sought to derive conservation laws under gradient flow from parameter
symmetries, but the scope and limitations of this connection remain unclear. We develop a unified
geometric framework that clarifies the precise relationship between the two notions, including the conditions under which
symmetries correspond to conservation laws. We introduce a notion of compositional identifiability and use it to establish a general inheritance principle for complete characterizations of
symmetries and conservation laws in multilayer networks. We apply the framework to multi-head and grouped-query
attention,
polynomial neural networks, and square deep linear networks.
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Artificial Intelligence
SGG-ReflAct: Sub-Goal Guided ReflAct with Structured Planning for Reliable Long-Horizon Reasoning
Jaeho Jung and Sung Hoon Jung
Recent advances in
reasoning backbones have empowered large
language model (LLM)agentstotackle complex, multi-step tasks. However, as
reasoning horizons grow, inconsistent internal beliefs induce intermediate errors that cause
agents to drift from their goals. This limitation also persists in REFLACT, which reflects only on the end-goal at each step without explicitly considering intermediate sub goals. To address this problem, we propose SGG-ReflAct (Sub-Goal Guided Re flAct), a
reasoning backbone that integrates sub-goals generated through a single path LLM planner into the reflection process. We further extend this framework to BeamSGG-ReflAct, which replaces the single-path planner with a beam search based LLM planner for structured plan exploration. We run experiments on ALF World, ScienceWorld, and Jericho with multiple LLM models. SGG-ReflAct out performs REFLACT in nearly all settings, achieving best success rate gains of 14.9 percentage points on ALFWorld and 8.0 percentage points on ScienceWorld with Llama-3.1-8B-Instruct. Our experimental analysis shows that SGG-ReflAct re duces hallucinated actions and achieves its largest gains on procedurally ordered tasks. Furthermore, experimental results with BeamSGG-ReflAct show that the backbone's effectiveness depends on plan quality: explicitly specifying the re quired operations recovers gains that plan searching alone cannot achieve. These results demonstrate that SGG-ReflAct offers a practical and highly effective rea soning backbone, enabling LLM
agents to achieve reliable performance in com plex, long-horizon tasks through easy integration.
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Computer Vision
ActionLens: Diagnosing Spatial-Temporal Binding Failures in Vision-Language Models
Gueter Josmy Faure, Min-Hung Chen, Hao Ping Wang et al. (+3 more)
Video-capable
vision-
language models score above 80% on popular
benchmarks yet struggle with spatial-temporal binding: associating the right action with the right person at the right moment. We introduce ActionLens, a diagnostic
benchmark of 6,701 multiple-choice
video questions spanning five targeted diagnostics: transition detection, actor-specific identification, concurrent action binding, directed interaction
reasoning, and gaze detection. Ground-truth answers are derived deterministically from 1.58 million per-second, per-person annotations. Fourteen rounds of human quality engineering raised answer clarity from 53% to above 90% human accuracy. Across 20 VLMs, the full-set leader scores 68.8%; on the human-reviewed subset, it scores 65.9% versus 91.0% for the pooled human reference. Gaze detection remains near chance against 89.6% human accuracy. On actor disambiguation, reference-interface controls show that relational descriptions recover 5.55–13.25 points over static coordinates, confirming a substantial numeric-parsing penalty; yet visual boxes still lead every model by 1.15–6.50 points, exposing a residual unboxed actor-resolution gap. A binding-trap analysis shows models systematically select the wrong actor's action. ActionLens provides diagnostic measurements of these distinct failure modes across model families and scales for direct comparison. We release all data, code, and evaluation scripts at https://anonymous.4open.science/r/lmms-eval-2276
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All authors Gueter Josmy Faure, Min-Hung Chen, Hao Ping Wang, Timothée Lardy, Hung-Ting Su, Winston H. Hsu
Artificial Intelligence
Remember Before You're Asked: MemDream for Self-Probing Memory Evolution
Mingfei Lu, Mengjia Wu, Runsong Jia et al. (+2 more)
Memory is essential for enabling LLM-based
agents to maintain coherent, personalized behavior over long-horizon interactions. However, existing
memory systems share a fundamental limitation: they never proactively test their own
memory, repairing it only after real queries expose weaknesses. This reactive paradigm means every
retrieval failure corresponds to a real interaction in which the cost has already been paid. We propose MemDream, a framework that enables self-probing
memory evolution for LLM
agents. Our framework periodically enters offline dream cycles where three specialized
agents (Dreamer, Analyst, Consolidator) collaboratively probe, diagnose, and repair the
memory graph before failures occur. A policy trained via Group Relative Policy Optimization learns which repair operations produce durable
retrieval improvements, while a soft decay mechanism provides reversible forgetting driven by the same anticipatory signal. Experiments on LoCoMo and MemoryAgentBench demonstrate that MemDream improves answer F1 by 4.5 points on LoCoMo and achieves a 9.1-point higher overall score on MAB over the strongest reactive-
evolution baselines.
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All authors Mingfei Lu, Mengjia Wu, Runsong Jia, Zhe Luo, Yi Zhang
Artificial Intelligence
APOLO: Automatic Prompt Optimization for Ontology Learning
Huu Tan Mai, Roman Kochnev, Cuong Xuan Chu et al. (+3 more)
Ontology Learning (OL) from text has advanced with the emergence of Large
Language Models (LLMs), but it remains challenging due to the limited availability of annotated training data and the difficulty of adapting LLMs to perform OL effectively. We address this via APOLO - Automatic
Prompt Optimization for Ontology Learning, by casting OL as an explicit
prompt optimization problem over LLM modules. To obtain training data, we employ a multi-
agent system that generates text-ontology pairs from existing expert-curated ontologies. We then propose two ontology learner architectures: a greedy and an autoregressive learner, and optimize both using GEPA, a greedy evolutionary
prompt optimizer built on DSPy. Experiments on two ontologies - a biomedical (DOID) and a plant ontology (PO) show consistent improvements after optimization across nearly all model and mode combinations, with autoregressive learners achieving the largest gains. Our results demonstrate that
prompt optimization is a viable and lightweight alternative to fine-tuning for OL, and that the autoregressive formulation better captures ontological structure than the greedy approach.
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All authors Huu Tan Mai, Roman Kochnev, Cuong Xuan Chu, Lukas Lange, Heiko Paulheim, Daria Stepanova
Computer Vision
Boyu Zhang, Yifan Liu, Shuxia Lin et al. (+3 more)
Diffusion Transformers (DiTs) have emerged as the dominant architecture for high-fidelity image and
video generation. Recent DiT systems increasingly use structured
prompts for training, improving caption quality and
prompt adherence. However, their generation quality can degrade severely under out-of-domain (OOD)
prompts, including the free-form descriptions supplied by users at inference time. Although LLM-based rewriting can convert these
prompts into structured formats, it does not guarantee that the rewritten
prompts align with the training distribution. Our analysis links this degradation to
attention sinks and reduced early-step image-to-text
attention and shows that sink suppression alone is insufficient to restore generation quality. Despite effective sink suppression, models trained with standard gated
attention exhibit reduced early-step image-to-text
attention and suboptimal generation quality. Based on these insights, we propose Timestep-Aware Gated
Attention (TSGate), which injects a timestep-conditioned bias into the gate signal so that gating behavior adapts across denoising steps. Extensive experiments show that TSGate consistently outperforms both the baseline and standard gated
attention across multiple
benchmarks, improving the raw-
prompt DPG score by 9.5% over the baseline.
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All authors Boyu Zhang, Yifan Liu, Shuxia Lin, Qingjian Ni, Yinfei Xu, Xu Yang
Machine Learning
Shallow Queries, Mature Values: Depth-Asynchronous Self-Speculation for Looped Transformers
Guanghao Li, Zihan Su, Hao Yu et al. (+6 more)
Looped
Transformers reuse a shared block across recurrent depths, making autoregressive decoding expensive because every generated
token requires many sequential recurrent passes. Self-speculative decoders reduce this cost by drafting at an early depth and verifying at full depth, but typically bind draft computation to prefix representations from the same recurrent depth. We find that queries and keys approach their final-depth representations earlier than values, and controlled prefix-channel interventions show that mature values substantially improve shallow draft predictions. Motivated by this asymmetry, we introduce Depth-Asynchronous Self-Speculation (DAS), which decouples the depth of draft computation from the depth of verified-prefix representations it reads. Its Mature-V primitive lets shallow queries retrieve full-depth prefix values without additional recurrent computation. We further develop DAS-Wave, which combines depth-asynchronous prefix reads with carried parallel refinement, progressive block growth, and an independent full-depth verifier. Across four recurrent-model checkpoints and mathematics and code workloads, DAS-Wave achieves 4.00–6.96$\times$ mean throughput speedup over paired full-depth autoregressive decoding in the same inference stack. These results identify prefix-information depth as an effective design axis for recurrent self-speculation.
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All authors Guanghao Li, Zihan Su, Hao Yu, Jinyang Jiang, Tao Ren, Zehao Li, Feng Lu, Ming Tang, Chun Yuan
Artificial Intelligence
The Marathon of Scientific Reasoning: Robustness of Scientific Agents to Perturbations in Multi-Turn Interactions
Xiaoting Lyu, Xinbo Ma, Yufei Han et al. (+5 more)
Large
language model (LLM)-based scientific
agents are increasingly used for scientific problem solving, yet their robustness to imperfections arising during multi-turn interactions remains poorly understood. We introduce SciARP (Scientific
Agent Robustness to Perturbations), a
benchmark for evaluating scientific
agents under scientifically plausible perturbations throughout multi-turn problem solving. SciARP transforms 620 scientific problems into interdependent tasks of 3–13 turns and defines 13 perturbation types spanning problem understanding, evidence processing,
reasoning, and conclusion formation. Clean and perturbed versions of each task are independently executed under matched settings, producing paired live trajectories for evaluating both task success and process reliability. Experiments across eight LLMs from four model families reveal three key robustness characteristics. First, different classes of scientific perturbations exhibit distinct robustness profiles and can decouple task progression from scientific reliability:
agents may continue advancing through the task even after their information or
reasoning has become unreliable. Second, stronger clean-task performance does not necessarily translate into stronger robustness, as models with higher clean-task accuracy can exhibit larger degradation under perturbation. Third, perturbation effects exhibit strong temporal dynamics: they may remain latent for multiple turns before emerging and subsequently propagate through downstream dependencies. Together, these findings show that current scientific
agents remain insufficiently robust to scientifically plausible perturbations, with failures often remaining undetected, propagating, and resisting recovery.
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All authors Xiaoting Lyu, Xinbo Ma, Yufei Han, Hangwei Qian, Ziyang Lin, Bin Wang, Bin Wang, Wei Wang
Computation and Language
Papers Without Code: Availability of GitHub Repositories Linked in *CL Publications
Selina Meyer and Michael Roth
Source code and data published at computational linguistics (*CL) venues are increasingly being shared via GitHub. While this generally is a favourable development for the accessibility and potential reusability of research artifacts in natural
language processing (NLP), the long-term availability of such repositories has not been evaluated. In this squib, we discuss the availability of repositories linked in papers published in the Computational Linguistics (CL) journal as well as at ACL and its co-located events over the past ten years. Contrary to our expectations, we find that GitHub repositories linked in more recent ACL publications are unavailable at similar rates as in older publications, in parts due to an increase in empty and placeholder repositories. Similar trends hold for other *CL venues, but not for platforms other than GitHub.
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